Immunetrics, Inc.

Our goal: Cure diseases with code. About us: Millions of people suffer from chronic diseases that lack effective, affordable, and targeted treatments. Traditional approaches to drug development and clinical trial design are slow, expensive, and prone to failure. Immunetrics reenvisions drug development for the 21st century, driven by the power of in silico modeling. We use both data and biological first principles to build efficient, detailed models of disease, and then apply these models to identify promising therapies and optimize patient treatment. For over 15 years we've been working towards this vision with our pharmaceutical clients, freely using mechanistic (Quantitative Systems Pharmacology) and data-driven modeling approaches. We focus on providing actionable, concrete predictions that inform real-world clinical decisions. Our team of software engineers works alongside biologists, engineers, and mathematicians to build and customize a simulation platform for pushing the frontiers of pharmaceutical science. About you: We're looking for pragmatic backend software engineers to join our efforts. Our engineers work on a wide range of projects, spanning domain-specific language development, high-performance scientific computing in cluster/cloud environments, development of analysis and simulation engines for scientific users, data analysis projects, and productizing feature requests from scientists. Working in a small team, you'll be involved end-to-end in the development process, from initial design to supporting our scientific users. Candidates should be proficient Python programmers, with strong OO design skills, and a commitment to good software engineering principles. Experience with numpy, pandas, scipy, PyTorch, scikit-learn, Jupyter, or other related scientific computing tools is helpful. Applicants should have a B.S. (or higher) degree in Computer Science or related field. Most importantly, candidates should have a desire to work in a small-company environment, using software to solve hard problems in medical science.